• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1181-1187
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• 1995

In video camera, an automatic video quality compensation method using the dynamic range transform is proposed. The histogram is used to decide the nonuniformness of picture brightness by nonuniform lighting. The gray level is divided four regions, and the histogram is obtained per one field. We introduce a new parameter, nonuniformness, defined by the cumulative difference between its CDF and LCDF. We also propose the decision function of the dynamic range transform constant versus its nonuniformness, and compensate the quality of video automatically.

• Journal of Integrative Natural Science
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• v.13 no.2
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• pp.83-86
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• 2020

In In this study, technique has been suggested according to the calculation of thresholds of histogram based on overlap areas for reducing noise while analyzing the functions of them. Suggested algorithm is to convert histogram extracted from color images to gray level and select overlap areas from extracted histogram. In addition, feature table is configured after extracting histogram in the relevant overlap area while comparing and retrieving for query and database video images by using this feature table. Suggested retrieval system has been confirmed to be more outstanding with retrieval function in video images with more noises than the system that only used color histogram.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.192-194
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• 2003

Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.417-422
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• 1999

Purpose : The aim of this study was to assess the validity of standard deviation of gray scale histogram in digital subtraction radiography as a test parameter for superimposition error. Materials and Methods : Twenty periapical radiographs were used as baseline images and they were copied to exclude the influence of exposure geomety and contrast differences. These subsequent images were linearly displaced by 0.1-0.5 mm in the x-. y- and xy-directions, rotated by 0.5-3° and distorted by angular contraction of 1-5° in x- and y-axis before subtraction. The standard deviations of gray levels in the subtraction images were obtained and paired t-tests were performed. Pearson correlation coefficients(r) were calculated between the standard deviations and the superimposition errors. Results : Linear displacement showed high correlation coefficients of 0.997, 0.997 and 0.995 in x-. y- and xy-axis respectively. Statistically significant different standard deviation existed among all linearly displaced groups(p<0.05). Distortion showed relatively low correlation coefficients of 0.982 and 0.959 in x- and y-axis. The standard deviations between the two distortion groups were statistically significant different(p<0.05). Conclusion : Standard deviation of gray level distribution in digital subtraction images is satisfactory but not perfect similarity measure to assess the superimposition errors.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.646-651
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• 2014

Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1409-1412
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• 1987

Median filter techniques were applied to the chest radiographs for the digital image processing. Histogram distributions were transformed to convert the gray level for detections of the small gray deviations of the image boundaries.

• ETRI Journal
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• v.29 no.5
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• pp.703-705
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• 2007

An image fidelity assessment using the edge histogram descriptor (EHD) of MPEG-7 is presented. Neither additional data nor fragile watermarking is needed, and there is no need to access the original image as a reference. Only the EHDs of the original image and the received image are required. The peak signal-to-noise ratio (PSNR) obtained by comparing the EHD extracted from the received image and that of the original image is used to assess the noise level of the received image. Experimental results show that the PSNRs calculated from the conventional pixel-to-pixel gray level and from the proposed bin-to-bin EHD maintain a proportional relationship. This implies that the EHD can be used instead of image data for the image fidelity assessments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2352-2355
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• 2003

In recent yeas, studies on computer aided diagnosis (CAD) using image analysis on CT images have been conducted with respect to various diseases. Extracting ground-glass opacities (GGO) on lung CT images is one of such subjects, though it has not found an established method yet. If the region of ground-glass opacities is large on CT images, it can be detected without much difficulty. On the other hand, if the region is small, it is still difficult to find it exactly. In the latter case, increasing overlooking possibility cannot be avoided according to smaller size of the region. To solve this difficulty, this paper proposes an automatic technique for extracting ground-glass opacities on lung CT images employing some statistical parameters of a gray level histogram and a differential histogram. The proposed technique is applied to some lung CT images in the performed experiment. The results are shown with discussion on future work.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.126-131
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• 1998

In computer vision system of modern industry precise measuring has lots of dfficulties because of measurement error due to distortion phenomenon. Among the difficulties, the distortion of edge is regraded as a dominent problem. which is caused by the vlurred image. The blurred image apperar when camera can not discriminate its precise focus. So. it is very important to decide focus of lens and to develop algorithm in order to correct distortion phenomenon. Thus. discrimination criteria obtained by image information of precise focus must be fixed in advance. The gray level histogram of image acquired from blurred edge tends to show a uniform distribution. Bimodal intensity histogram is related with condition of focus, and it is possible to find good condition of focus by using bimodal histogram of entropy.

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2006.11a
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• pp.90-90
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• 2006